Systems and methods for networked sensor nodes

ABSTRACT

A sensor assembly includes an impedance sensor element, an impedance sensor reader and a communications module. The communications module is configured to communicate with a remote computing device. The impedance sensor reader is coupled to the impedance sensor element. The impedance sensor reader includes a synthesizer and a detector. The synthesizer is configured to output an excitation signal having known values for a plurality of signal characteristics to the impedance sensor element and to generate the excitation signal based on a plurality of direct digital synthesizer (DDS) coefficients received from the remote computing device through the communications module. The detector is coupled to the impedance sensor element and configured to detect a response of the impedance sensor element to the excitation signal and determine an impedance of the impedance sensor element based at least in part on the response of the impedance sensor element to the excitation signal.

CROSS-REFERENCE TO RELATED APPLICATIONS

The Application is a continuation of U.S. patent application Ser. No.14/982,996, filed Dec. 29, 2015, entitled “SYSTEMS AND METHODS FORNETWORKED SENSOR NODES,” which is hereby incorporated by reference inits entirety.

BACKGROUND

The field of this disclosure relates generally to networked sensor node,and more particularly, to systems and methods for self-powered networkedsensor nodes.

Networked sensor nodes with chemical sensors can be deployed in avariety of environments for sensing contaminants and concentration ofparticles that need to be tracked in the environment. Some known systemsuse sensors that detect chemicals based on a change in impedance of thesensor. The impedance of such sensors varies as a function of the sizeand concentration of molecules adsorbed on the surface. To detect thepresence and concentration of the target chemical(s), the sensordetermines the impedance of the sensor using a known excitation signal.Generating the known excitation signal is often computationallyintensive and requires significant processing and electrical power.

Some known networked sensor nodes would be useful for placement aroundgas well pads to detect gas leaks. The network of such sensors may bedeployed to cover a wide area around a well, so that maps of the gasleaks over the entire site can be collected and location of thermogenicleaks may be triangulated. In such installations, providing utility gridconnections for each sensor node may be impossible or impractical.Moreover, powering known sensors with a stored energy device, such as abattery, requires undesirable maintenance to periodically replace thebatteries. Additionally, batteries and other localized power sources maynot be able to provide sufficient power for some known sensors unlessthe power source is increased to an undesirably large size.

BRIEF DESCRIPTION

In one aspect, a sensor assembly includes an impedance sensor element,an impedance sensor reader and a communications module. Thecommunications module is configured to communicate with a remotecomputing device. The impedance sensor reader is coupled to theimpedance sensor element. The impedance sensor reader includes asynthesizer and a detector. The synthesizer is configured to output anexcitation signal having known values for a plurality of signalcharacteristics to the impedance sensor element. The synthesizer isconfigured to generate the excitation signal based on a plurality ofdirect digital synthesizer (DDS) coefficients received from the remotecomputing device through the communications module. The detector iscoupled to the impedance sensor element. The detector is configured todetect a response of the impedance sensor element to the excitationsignal and determine an impedance of the impedance sensor element basedat least in part on the response of the impedance sensor element to theexcitation signal.

In another aspect, a sensor network or a “mesh network” includes aplurality of sensor assemblies and a remote computing device. Eachsensor assembly includes an impedance sensor element coupled to animpedance sensor reader, and a communications module. The impedancesensor reader is configured to generate an excitation signal based on aplurality of direct digital synthesizer (DDS) coefficients and determinean impedance of the impedance sensor element based at least in part on aresponse of the impedance sensor element to the excitation signal. Thecomputing device is communicatively coupled to at least one sensorassembly. The computing device includes a processor and a memory device.The memory device stores instructions to cause the computing device todetermine a plurality of DDS coefficients for at least one sensorassembly and deliver the plurality of DDS coefficients to the at leastone sensor assembly. The plurality of DDS coefficients are determined toproduce the excitation signal in the at least one sensor assembly.

In a further aspect, a method of operating a sensor node including animpedance sensor element includes receiving, by the sensor node, aplurality of direct digital synthesizer (DDS) coefficients from a remotecomputing device. The sensor node stores the plurality of DDScoefficients in a memory device. The sensor node generates an excitationsignal based on the plurality of DDS coefficients and a clock signal.The sensor node determines an impedance of the impedance sensor elementbased at least in part on the response of the impedance sensor elementto the excitation signal.

DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a block diagram of an exemplary sensor network;

FIG. 2 is a block diagram of an exemplary computing device for use inthe network shown in FIG. 1;

FIGS. 3 and 4 are block diagrams of exemplary sensor assemblies for usein the network shown in FIG. 1;

FIG. 5 is an exemplary impedance sensor element for use in the sensorassemblies shown in FIGS. 3 and 4;

FIG. 6 is another exemplary impedance sensor element for use in thesensor assemblies shown in FIGS. 3 and 4 and including an interdigitatedelectrode structure disposed on a substrate.

FIG. 7 is an exemplary sensor assembly for use in the network shown inFIG. 1;

FIG. 8 is an exemplary power module for use in the sensor assembly shownin FIG. 7;

FIG. 9 is another exemplary sensor assembly for use in the network shownin FIG. 1;

FIG. 10 is a graph of simulated power requirements as a function ofresolution for three exemplary sensor assemblies;

FIG. 11 is a graph of the result of applying a developed transferfunction to display methane concentrations as measured with an exemplarymethane sensor; and

FIG. 12 is a graph of the result of outdoors stand-off detection ofmethane leaks with the calibrated exemplary methane sensor during about10 minutes time of measurements.

Unless otherwise indicated, the drawings provided herein are meant toillustrate features of embodiments of the disclosure. These features arebelieved to be applicable in a wide variety of systems comprising one ormore embodiments of the disclosure. As such, the drawings are not meantto include all conventional features known by those of ordinary skill inthe art to be required for the practice of the embodiments disclosedherein.

DETAILED DESCRIPTION

In the following specification and the claims, reference will be made toa number of terms, which shall be defined to have the followingmeanings.

The singular forms “a”, “an”, and “the” include plural references unlessthe context clearly dictates otherwise.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where the event occurs and instances where it does not.

Approximating language, as used herein throughout the specification andclaims, may be applied to modify any quantitative representation thatcould permissibly vary without resulting in a change in the basicfunction to which it is related. Accordingly, a value modified by a termor terms, such as “about”, “approximately”, and “substantially”, are notto be limited to the precise value specified. In at least someinstances, the approximating language may correspond to the precision ofan instrument for measuring the value. Here and throughout thespecification and claims, range limitations may be combined and/orinterchanged, such ranges are identified and include all the sub-rangescontained therein unless context or language indicates otherwise.

As used herein, the terms “processor” and “computer” and related terms,e.g., “processing device”, “computing device”, and “controller” are notlimited to just those integrated circuits referred to in the art as acomputer, but broadly refers to a microcontroller, a microcomputer, aprogrammable logic controller (PLC), an application specific integratedcircuit (ASIC), and other programmable circuits, and these terms areused interchangeably herein. In the embodiments described herein, memorymay include, but is not limited to, a computer-readable medium, such asa random access memory (RAM), and a computer-readable non-volatilemedium, such as flash memory. Alternatively, a compact disc-read onlymemory (CD-ROM), a magneto-optical disk (MOD), and/or a digitalversatile disc (DVD) may also be used. Also, in the embodimentsdescribed herein, additional input channels may be, but are not limitedto, computer peripherals associated with an operator interface such as amouse and a keyboard. Alternatively, other computer peripherals may alsobe used that may include, for example, but not be limited to, a scanner.Furthermore, in the exemplary embodiment, additional output channels mayinclude, but not be limited to, an operator interface monitor.

Further, as used herein, the terms “software” and “firmware” areinterchangeable, and include any computer program stored in memory forexecution by personal computers, workstations, clients and servers.

As used herein, the term “non-transitory computer-readable media” isintended to be representative of any tangible computer-based deviceimplemented in any method or technology for short-term and long-termstorage of information, such as, computer-readable instructions, datastructures, program modules and sub-modules, or other data in anydevice. Therefore, the methods described herein may be encoded asexecutable instructions embodied in a tangible, non-transitory, computerreadable medium, including, without limitation, a storage device and/ora memory device. Such instructions, when executed by a processor, causethe processor to perform at least a portion of the methods describedherein. Moreover, as used herein, the term “non-transitorycomputer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices,including, without limitation, volatile and nonvolatile media, andremovable and non-removable media such as a firmware, physical andvirtual storage, CD-ROMs, DVDs, and any other digital source such as anetwork or the Internet, as well as yet to be developed digital means,with the sole exception being a transitory, propagating signal.

Embodiments of the present disclosure relate to networked sensor nodes,and more particularly, to self-powered networked sensor nodes. Thesensor nodes described herein determine an impedance of an impedancesensor element using a predetermined input signal generated using directdigital synthesis (DDS). The DDS coefficients for the sensor nodes arecalculated by a remote computing device and transmitted to each node toreduce the computational and electric load of sensor nodes. Moreover,various other power saving techniques are implemented in the sensornodes to further reduce the power requirements of the sensor nodes.These relatively low power sensor nodes are self-powered by a localizedpower source, such as a solar panel and a rechargeable battery.

As used herein, the term “impedance sensor” or “impedance sensorelement” means a sensor that has an equivalent circuit that includes atleast one capacitor (C) component and at least one resistor (R)component. The R and C components in concert provide a RC circuit thathas an impedance spectrum. The impedance spectrum of the RC circuit isaffected by the changes in C and R components of the circuit. The“impedance sensor” may have an equivalent RC circuit that has more thanone set of R and C components. For example, it may have two sets of Rand C components or three sets of R and C components. The impedancespectrum of the RC circuit with more than one set of R and C componentsis affected by the changes in several C and several R components of thecircuit.

In some embodiments, the impedance sensor may also have an inductance(L) component. Such an impedance sensor has an equivalent circuit thatincludes inductance (L) and at least one capacitor (C) and at least oneresistor (R) components. When “impedance sensor element” includes atleast one inductance (L), at least one capacitor (C), and at least oneresistor (R) components, it may be referred to as a “resonant sensorelement”.

A sensor with an equivalent circuit that includes at least oneinductance (L), at least one capacitor (C), and at least one resistor(R) components may operate (or “resonate”) at one or more frequencies ina frequency range of analysis. A signal may be received from the sensoracross the frequency range of analysis. The signal may be affected bythe environment around the sensor. For example, the signal includesinformation about a sensor in contact with the fluid. One or moreproperties of the fluid may be determined based at least in part on theimpedance spectra. The impedance sensors may be used to measure avariety of physical, chemical and/or biological parameters.

The sensor assembly (sometimes also referred to as a “sensor system” ora “sensor node”) is intended to analyze an industrial fluid to determineone or more properties of the fluid such as external contaminants of thefluid and/or fluid aging based on the analyzed response of the impedancesensor. Nonlimiting examples of external contaminants are gases,particles, ions. Nonlimiting examples of aging are thermal degradationproducts of industrial fluid, oxidation degradation products ofindustrial fluid, ultraviolet degradation products of industrial fluid.

The sensor assembly may be a part of a mobile or stationary consumerelectronics system or a part of a consumer system. Nonlimiting examplesof the consumer electronics systems are smart phones, smart watches,tablets, textile-based wearable consumer electronics systems, and othersknown in the art.

Nonlimiting examples of the consumer systems are vehicles, automobiles,boats, furniture, homes, clothing, diapers and other components of babycare and other re-usable and disposable consumer systems.

A communications module may be an integral part of the mobile orstationary consumer electronics system. The communications module mayserve communications function for a variety of components of a mobile orstationary consumer electronics system. Nonlimiting examples of suchcomponents of a mobile or stationary consumer electronics system aretelephone component, global positioning system (GPS) component, andimpedance sensor component.

In an aspect, the industrial fluid of interest is at least one ofambient air at an industrial site, an oil, a fuel, a solvent, a solid,and a gas. Non-limiting examples of an industrial site includemanufacturing facility, processing facility, disposal facility,industrial research facility, gas producing facility, oil producingfacility, and others.

In some embodiments, a sensor according to this disclosure analyzes anindustrial fluid, such as ambient air at an industrial site. Detectionof methane and other gases may be performed using a sensor assembly.Detection of several gases or several other industrial fluids or theirpatterns or their signatures may be performed using a sensor assembly.

In some embodiments, a sensor assembly and/or a network of sensorassemblies or sensor nodes is performing monitoring of industrialprocess. Nonlimiting examples of industrial process include productionof raw gas, material extraction, material transport, production of rawoil, operation of an internal combustion engine, operation of anoil-filled transformer, a chemical reaction process, a biologicalreaction process, purification and/or separation process, a catalyticprocess, a general combustion process, and other industrial processes.

Some embodiments of an impedance sensor or impedance sensor element ofthis disclosure includes at least two partially or fully independentoutputs in response to an industrial fluid. Such a sensor is sometimesreferred to as a “multivariable sensor”. An example multivariable gassensor includes a sensing material with diverse responses to differentgases, a multivariable sensor element with different outputs torecognize these different responses, and data analytics to provideaccurate gas quantitation based on the outputs. An example multivariablebiological sensor includes a sensing material with diverse responses todifferent biological species, a multivariable sensor element withdifferent outputs to recognize these different responses, and dataanalytics to provide accurate quantitation based on the outputs. Anexample multivariable physical sensor includes a multivariable sensorelement with different outputs to different physical effects from theindustrial fluid. Nonlimiting examples of such physical effects may bedielectric constant, conductivity, temperature, pressure of theindustrial fluid.

The multivariable response of the sensor may be analyzed by multivariateanalysis. Nonlimiting examples of multivariate analysis of multivariableresponse of the sensor may be Principal Components Analysis (PCA),Independent Component Analysis (ICA), Linear Discriminant Analysis(LDA), and Flexible Discriminant Analysis (FDA), Partial Least Squares(PLS), and many others known in the art.

FIG. 1 is a block diagram of a sensor network 100. Sensor network 100includes a group 102 of sensor assemblies 104 and a remote computingdevice 106. Although five sensor assemblies 104 are shown in FIG. 1,group 102 may include more or fewer sensor assemblies 104.

In the exemplary embodiment, sensor assemblies 104 each include achemical sensor such as an impedance sensor element (not shown inFIG. 1) to sense, for example, industrial fluid. Sensor assemblies 104are in direct or indirect communication with remote computing device106. In the exemplary embodiment, sensor assemblies 104 communicateusing any suitable wireless communication protocol. In otherembodiments, sensor assemblies 104 communicate using a wiredcommunication protocol. Communication with remote computing device 106allows sensor assemblies 104 to change data acquisition parameters forthe impedance sensor element and to provide sensor data to remotecomputing device 106. The sensor data may be collected sensor data, datadetermined from collected sensor data, or both collected and determinedsensor data. The collected sensor data includes the raw values receivedfrom the sensor. Determined sensor data may be calculated from the rawvalues received from the sensor, from the raw values received from morethan one sensor, from other determined sensor data, or both. Sensorassemblies 104 may include sensors of the same kind or of differentkinds. Sensors can be configured to sense same of different measurandsincluding methane, carbon monoxide, hydrocarbons, temperature, humidity,and other environmental parameters.

In the exemplary embodiment, the chemical sensor is an impedance sensorelement and the collected sensor data is a response of the impedancesensor element to a predetermined input signal (also referred tosometimes herein as an excitation signal and a known input signal). Inthe exemplary embodiment, the excitation signal is a sinusoidal signalwith a known frequency and amplitude. The response of the impedancesensor element to the known input signal is used calculate the impedanceof the impedance sensor element. The impedance of the impedance sensorelement changes as a function of the size and concentration of moleculesadsorbed on the surface of the impedance sensor element. A concentrationof gas of interest in the environment (industrial fluid) around aparticular one of sensor assemblies 104 is calculated by comparing thecalculated impedance of the sensor assembly's impedance sensor elementto an initial impedance of that impedance sensor element. In anexemplary embodiment, each sensor assembly 104 determines the impedanceof its impedance sensor element and transmits the calculated impedanceto remote computing device 106. In the exemplary embodiment, thechemical sensor is an impedance sensor element where the collectedsensor data is a response of the impedance sensor element to apredetermined input signal.

Each sensor assembly 104 generates its own predetermined input signal inthe exemplary embodiment. The signal is generated by direct digitalsynthesis (DDS) using a DDS synthesizer (not shown in FIG. 1). Thus,each sensor assembly is operable to know the specific characteristics,such as amplitude and frequency for example, of the predetermined inputsignal. DDS coefficients are used by the DDS synthesizer to generate thepredetermined input signal. The DDS coefficients for each sensorassembly 104 are calculated by remote computing device 106 andtransmitted to the sensor assemblies 104. In the exemplary embodiment,remote computing device 106 calculates separate DDS coefficients foreach sensor assembly 104 and transmits the DDS coefficients to theparticular sensor assembly 104 for which they were calculated. In someother embodiments, remote computing device 106 calculates a set of DDScoefficients to be used by more than one sensor assembly 104 (includingpossibly all sensor assemblies 104) and transmits the set of DDScoefficients to the sensor assemblies 104 for which they werecalculated.

The DDS coefficients are calculated, in some embodiments, to account forone or more real world, operational characteristic of a sensor assembly104. For example, if a sensor assembly 104, or a portion of the sensorassembly 104, is prone to second harmonic distortion, the DDScoefficients may be determined to account for and attempt to reduce thesecond harmonic distortion. The DDS coefficients may be predistortedbased on the difference between an actual operational characteristic ofa sensor assembly 104 and an ideal (or theoretical) operationalcharacteristic of that sensor assembly 104. The DDS coefficients can bepredistorted, for example, to account for the difference between idealwires with no resistance or inductance and actual real world wires thathave both resistance and inductance. This permits the sensor assembly104 to treat wires (and other components and circuits) as idealcomponents, thereby simplifying the calculations to be performed by thesensor assembly 104. In some embodiments, remote computing device 106relies on quarter wave symmetry of the predetermined input signal topermit reduction in the required DDS coefficients.

In the exemplary embodiment, sensor assemblies 104 are communicablycoupled to each other to form a wireless mesh network. At least one ofthe sensor assemblies 104 is additionally coupled in wired or wirelesscommunication with remote computing device 106. Communications from asensor assembly 104 to remote computing device 106 may be passed throughone or more other sensor assemblies 104 to reach remote computing device106. Similarly, communications from remote computing device 106 may bepassed through one or more other sensor assemblies 104 to reach thesensor assembly 104 that is the target of the communication. In otherembodiments, each sensor assembly 104 is communicatively coupled toremote computing device 106, such as in a star network configuration,and communicates with remote computing device 106 without the assistanceof other sensor assemblies 104. In still other embodiments, one or moreof sensor assemblies 104 is coupled in communication with a gatewaydevice (not shown) that is coupled in communication with remotecomputing device 106.

A user (not shown) may use remote computing device 106 to monitor any orall of sensor assemblies 104, the collected sensor data, and thedetermined sensor data. As described above, in some embodiments, remotecomputing device 106 receives the collected sensor data from sensorassemblies 104 and calculates the determined sensor data based on thecollected sensor data. In some embodiments, remote computing device 106is configured to generate an alert based on the collected sensor data,the determined sensor data, or a combination of the collected sensordata and the determined sensor data. The alert may be a humanperceivable alert, such as an audible or visible alarm, or an alert thatis not perceivable by humans, such as an alert directed at anothercomputing device. The alert may be triggered, for example, when theimpedance reported by one of the sensor assemblies 104 exceeds athreshold, when the change in the impedance of one of the sensorassemblies 104 exceeds a threshold, or when the determined dataassociated with one of the sensor assemblies 104 indicates a gasconcentration or presence exceeding a threshold. Remote computing device106 may be located any suitably distance from sensor assemblies 104. Inan example embodiment, sensor assemblies 104 and remote computing device106 are located around a natural gas well pad. In some otherembodiments, sensor assemblies 104 are located around a natural gas wellpad and remote computing device 106 is located at another location (suchas in a building meters or kilometers away). Moreover, in someembodiments, remote computing device is located anywhere in the worldand communicatively connected to sensor assemblies 104 by acommunications network, such as the Internet.

FIG. 2 is a block diagram of an exemplary computing device 200 that maybe used in sensor network 100 (shown in FIG. 1) as remote computingdevice 106 (shown in FIG. 1), as part of sensor assemblies 104 (shown inFIG. 1), or both. In the exemplary embodiment, computing device 200includes a memory 206 and a processor 204 that is coupled to memory 206for executing programmed instructions. Processor 204 may include one ormore processing units (e.g., in a multi-core configuration). Computingdevice 200 is programmable to perform one or more operations describedherein by programming memory 206 and/or processor 204. For example,processor 204 may be programmed by encoding an operation as one or moreexecutable instructions and providing the executable instructions inmemory device 206. The executable instructions, when executed byprocessor 204, cause processor 204 to perform the operations encodedtherein.

Processor 204 may include, but is not limited to, a general purposecentral processing unit (CPU), a microcontroller, a reduced instructionset computer (RISC) processor, an application specific integratedcircuit (ASIC), a programmable logic circuit (PLC), and/or any othercircuit or processor capable of executing the functions describedherein. The methods described herein may be encoded as executableinstructions embodied in a computer-readable medium including, withoutlimitation, a storage device and/or a memory device. Such instructions,when executed by processor 204, cause processor 204 to perform at leasta portion of the methods described herein. The above examples areexemplary only, and thus are not intended to limit in any way thedefinition and/or meaning of the term processor.

Memory device 206, as described herein, is one or more devices thatenable information such as executable instructions and/or other data tobe stored and retrieved. Memory device 206 may include one or morecomputer-readable media, such as, without limitation, dynamic randomaccess memory (DRAM), static random access memory (SRAM), a solid statedisk, and/or a hard disk. Memory device 206 may be configured to store,without limitation, maintenance event log, diagnostic entries, faultmessages, and/or any other type of data suitable for use with themethods and systems described herein.

In the illustrated embodiment, computing device 200 includes apresentation interface 208 that is coupled to processor 204.Presentation interface 208 outputs (e.g., display, print, and/orotherwise output) information such as, but not limited to, installationdata, configuration data, test data, error messages, and/or any othertype of data to a user 214. For example, presentation interface 208 mayinclude a display adapter (not shown in FIG. 2) that is coupled to adisplay device, such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), a light-emitting diode (LED) display, an organic LED(OLED) display, and/or an “electronic ink” display. In someimplementations, presentation interface 208 includes more than onedisplay device. In addition, or in the alternative, presentationinterface 208 may include a printer. In other embodiments, computingdevice does not include presentation interface 208 and/or is not coupledto a display device.

In the exemplary embodiment, computing device 200 includes an inputinterface 210 that receives input from user 214. For example, inputinterface 210 may be configured to receive selections, requests,credentials, and/or any other type of inputs from user 214 suitable foruse with the methods and systems described herein. In the exemplaryimplementation, input interface 210 is coupled to processor 204 and mayinclude, for example, a keyboard, a card reader (e.g., a smartcardreader), a pointing device, a mouse, a stylus, a touch sensitive panel(e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, aposition detector, and/or an audio input interface. A single component,such as a touch screen, may function as both a display device ofpresentation interface 208 and as input interface 210. In otherembodiments, computing device does not include input interface 210 orincludes an input interface for receiving instructions from anothercomputing device 200.

In the exemplary embodiment, computing device 200 includes acommunication interface 212 coupled to memory 206 and/or processor 204.Communication interface 212 is coupled in communication with one or moreremote device, such as another computing device 200, sensor assembly104, etc. Communication interface 212 may include, without limitation, awired network adapter, a wireless network adapter, an input/output port,analog to digital input/output port, and a mobile telecommunicationsadapter. Although a single communication interface 212 is shown in FIG.2, in other embodiments, computing device 200 includes more than onecommunication interface 212.

Instructions for operating systems and applications are located in afunctional form on non-transitory memory 206 for execution by processor204 to perform one or more of the processes described herein. Theseinstructions in the different implementations may be embodied ondifferent physical or tangible computer-readable media, such as memory206 or another memory, such as a computer-readable media 218, which mayinclude, without limitation, a flash drive, CD-ROM, thumb drive, etc.Further, instructions are located in a functional form on non-transitorycomputer-readable media 218, which may include, without limitation, aflash drive, CD-ROM, thumb drive, etc. Computer-readable media 218 isselectively insertable and/or removable from computing device 200 topermit access and/or execution by processor 204. In one example,computer-readable media 218 includes an optical or magnetic disc that isinserted or placed into a CD/DVD drive or other device associated withmemory 206 and/or processor 204. In some instances, computer-readablemedia 218 may not be removable.

FIGS. 3 and 4 is a block diagram of an exemplary sensor assembly 300that may be used as sensor assembly 104 in sensor network 100 (bothshown in FIG. 1). Sensor assembly 300 includes an impedance sensorelement 302, an impedance sensor reader 306, and a communications module304. Sensor assembly 300 communicates with a remote computing device,such as remote computing device 106.

FIG. 4 is a block diagram of another exemplary sensor assembly 300 thatmay be used as sensor assembly 104 in sensor network 100 (both shown inFIG. 1). Sensor assembly 300 includes an impedance sensor element 302, acommunications module 304, an impedance sensor reader 306, a powermodule 308, and a reset control module 310.

In some embodiments, the communications module 304 includes a radio, aradio frequency (RF) front end, and an antenna assembly (not shown). Theradio is configured for communication according to one or multiplewireless protocols. The radio is also configured to packetize of sensordata for transmission. The RF front end performs filtering and controlsthe reception and transmission of wireless signals. The antenna assemblyincludes one or more antennas configured to transform signals toelectromagnetic signals for sending and receiving data wirelessly.

In an example, a portion of computing may be performed in a remotecomputing device and a portion of computing may be performed in animpedance sensor reader. In another example, computing may be performedin an impedance sensor reader.

In the exemplary embodiment, impedance sensor element 302 is a resonantsensor element, such as a passive radio frequency identification (RFID)sensor element. In other embodiments, impedance sensor elements 302include inductor-capacitor-resistor (LCR) sensors, thickness shear mode(TSM) resonator sensors, acoustic wave (AW) sensors, surface acousticwave (SAW) sensors, tuning fork sensors, or split ring resonator (SRR)sensors. The impedance of impedance sensor element 302 is changed by thepresence of analytes in the environment (such as industrial fluid)around resonant sensor element 302. As used herein, the term “analyte”refers to a substance that includes any desirable measured environmentalparameter. As described herein, sensor assembly 300 determines thechanged impedance of impedance sensor element 302 to determine theamount of a particular analyte present in the environment. Moreover, insome embodiments, impedance sensor element 302 is differently responsiveto different analytes, and the amount of more than one analyte presentis determined by sensor assembly 300.

Communications module 304 is configured to communicate with a remotedevice, such as remote computing device 106 (shown in FIG. 1) or anothersensor assembly 300. In the exemplary sensor assembly 300,communications module 304 is a wireless communications module configuredfor wireless communication between sensor assembly 300 and one or moreremote device. In some embodiments, communications module 304 isconfigured for communication with both remote computing device 106 andother sensor assemblies 104. In some embodiments of sensor network 100,only one sensor assembly 104, 300 is configured to communicate, usingcommunications module 304, with both remote computing device 106 andother sensor assemblies 104, 300. In such embodiments, sensor network100 is configured in a mesh network and all other sensor assemblies 104,300 are configured to communicate, using communications module 304, onlywith other sensor assemblies 104, 300. In other mesh network embodimentsof sensor network 100, more than one sensor assembly 104, 300 isconfigured to communicate, using communications module 304, with bothremote computing device 106 and other sensor assemblies 104, 300.

Impedance sensor reader 306 is coupled to impedance sensor element 302and configured to detect the impedance of impedance sensor element 302.Impedance sensor reader 306 includes a synthesizer and a detector(neither shown in FIG. 4). The synthesizer is configured to output tothe impedance sensor element 302 the excitation signal having knownvalues for a plurality of signal characteristics, such as amplitude,phase, and frequency. The synthesizer is configured to generate theexcitation signal based on a plurality of DDS coefficients received fromremote computing device 106 through the communications module 304. Thedetector is configured to detect a response of impedance sensor element302 to the excitation signal and determine the impedance of theimpedance sensor element 302 based on the response. To get an accurateestimate of the impedance, the detection electronics may track theamplitude and the phase of the response.

Power module 308 provides the power for operation of sensor assembly300. Power module 308 includes a power source and a power modulecontroller (neither shown in FIG. 4). Sensor assembly 300 isself-powered and the power source includes a photovoltaic (PV) powersource and an energy storage device in the exemplary embodiment. Inother embodiments, the power source includes other miniaturized powersources such as ultrasonic receivers integrated with the sensor nodewhich can be powered from the sensor hub with a focused ultrasonic beam,or any other suitable localized power source. In some embodiments, thepower source is an energy harvesting source based on nonlimiting knowntypes of energy (e.g. solar, photonic, thermal, mechanical, vibrational,ambient radio-frequency). The energy storage device is a battery, acapacitor, a supercapacitor, any other suitable energy storage device orcombination of energy storage devices. In still other embodiments, thepower source is a remote power source, such as a utility grid. In theexemplary embodiments, sensor assemblies 300 are self-powered and eachsensor assembly includes its own power source. In other embodiments, apower source, such as an array of PV modules, acts as the power sourcefor more than one sensor assembly 300. The power module controllercontrols operation of power module 308 to produce, store, and deliverpower appropriately. In the exemplary embodiment, the power modulecontroller monitors the charging and discharging rate of the energystorage device and coordinates power saving operations. The power modulealso controls, when applicable, the power source and the powerconversion processes. For example, when the power source includes a PVmodule, the power module provides maximum power point tracking (MPPT) toattempt to maximize the power produced by the PV module.

Reset control module 310 is configured to use power from power module308 to reset impedance sensor element 302. As described above, theimpedance of impedance sensor element 302 changes in the presence ofgases in the environment due to adsorption of the gas on the impedancesensor element 302. Reset control module 310 periodically provides arelatively large current of electricity to impedance sensor element 302.The large electrical current through the resistive impedance sensorelement 302 generates heat that increases the temperature of impedancesensor element 302 and causes adsorbed gases to be released. Ideally,the reset will release all adsorbed gas and return impedance sensorelement 302 to its initial impedance. In some embodiments, reset controlmodule 310 compares the impedance of impedance sensor element 302 to areference impedance at a known frequency to determine whether or not thereset was successful. If the difference between the reference impedanceand the post-reset impedance of impedance sensor element 302 exceeds athreshold, for example and without limitation, more than 1%, more than5%, or more than 10%, reset control module 310 reattempts to resetimpedance sensor element 302. Reset control module includes instructionto attempt to save power and help attempt to minimize power consumptionof sensor assembly 300. For example, because a reset requires arelatively large electrical current, reset control module 310 isconfigured in some embodiments to delay initiating a reset until apredetermined amount of power is being produced or until a predeterminedcharge status of the energy storage device is met. In some embodiments,the impedance sensor reader includes a processor to compute response ofthe impedance sensor element.

FIGS. 5 and 6 illustrate non-limiting examples of designs of electrodesof an impedance sensor element.

FIG. 5 is an example multivariable sensor element 400 for use asimpedance sensor element 302 (shown in FIGS. 3 and 4). Sensor element400 is a passive RFID sensor with an antenna circuit 404 disposed on asubstrate 406. Antenna circuit 404 is covered at least partially by asensing film 408.

The RFID sensor is an RFID tag with an added sensing function. Antennacircuit 404 of the RFID tag performs sensing functions by changing itsimpedance parameters as a function of environmental changes. The antennacircuit 404 is the sensing region. The determination of environmentalchanges is performed by analysis of impedance of multivariable resonantsensor element 400. The material changes in the resonant sensor element400, and particularly changes to antenna circuit 404, upon exposure toan analyte are measured. Dielectric, dimensional, charge transfer, andother changes of materials properties may be detected by the changes inthe resonant properties of resonant sensor element 400.

Sensing film 408 is disposed on antenna circuit 404 to alter theimpedance response of multivariable resonant sensor element 400.Depositing sensing film 408 onto antenna circuit 404 creates an RFIDchemical, biological, or physical sensors. In another approach, acomplementary sensor may be attached across an antenna and an optionalmemory chip. The complementary sensor may be used to alter sensorimpedance response. Non-limiting examples of such sensors are describedin U.S. Pat. No. 7,911,345 entitled “Methods and systems for calibrationof RFID sensors”. As used herein the term “sensing materials and sensingfilms” includes, but is not limited to, materials deposited onto theRFID sensor to perform the function of predictably and reproduciblyaffecting the complex impedance sensor response upon interaction withthe environment. For example, a conducting polymer such as polyanilinechanges its conductivity upon exposure to solutions of different pH.When such a polyaniline film is deposited onto multivariable resonantsensor element 400, the complex impedance sensor response changes as afunction of pH. Thus, such an RFID sensor works as a pH sensor. Whensuch a polyaniline film is deposited onto the RFID sensor for detectionin gas phase, the complex impedance sensor response also changes uponexposure to basic (for example, NH3) or acidic (for example HC1) gases.Sensor films include, but are not limited to, polymer, organic,inorganic, biological, composite, and nano-composite films that changetheir electrical and or dielectric property based on the environmentthat they are placed in. Non-limiting additional examples of sensorfilms may be a sulfonated polymer such as Nafion, an adhesive polymersuch as silicone adhesive, an inorganic film such as sol-gel film, acomposite film such as carbon black-polyisobutylene film, ananocomposite film such as carbon nanotube-Nafion film, goldnanoparticle-polymer film, metal nanoparticle-polymer film, metal-oxide,cryptophane, metal-organic framework, carbon nanoparticles, graphene,molybdenum disulfide, electrospun polymer nanofibers, electrospuninorganic nanofibers, electrospun composite nanofibers, and any othersensor material. In order to prevent the material in the sensor filmfrom leaking into the liquid environment, the sensor materials areattached to the sensor surface using standard techniques, such as,without limitation, covalent bonding, and electrostatic bonding.Additional sensors, sensor assemblies, and impedance measurementtechniques capable of being used in sensor assemblies of the presentdisclosure are described in U.S. Pat. App. Pub. No. 2014/0095102entitled “Systems and Methods of Monitoring Sensors”, and U.S. Pat. App.Pub. No. 2014/0091811 entitled “Systems and Methods of MonitoringSensors”.

FIG. 6 is an example of impedance sensor element 410 for use asimpedance sensor element 302 (shown in FIGS. 3 and 4). Sensor element410 includes an interdigital (interdigitated) electrode structure 412disposed on a substrate 414. The interdigitated electrode structure 412is the sensing region. The interdigitated electrode structure 412 iscovered at least partially by a sensing film (not shown). Thisinterdigital electrode structure has an electrode width 416 and anelectrode spacing 418 (also referred to as a gap) between electrodes420. The spacing between electrodes 420 can be the same or different indifferent directions. Nonlimiting examples of a sensing region areinterdigitated two-electrode structures with the electrode width and thespacing between electrodes in the range from 1 nanometer and 1centimeter and sensing area in the range from 1 square micrometer to 10square centimeters. In an exemplary embodiment, the electrode width 416and electrode spacing 418 are both about 0.45 mm.

Other suitable interdigitated electrodes include electrode structureswith variable electrode width and spacing, tapered electrodes, circularelectrodes, and others known in the art. Although two electrodes 420 areshown, other embodiments include four electrodes 420 or more than fourelectrodes 420.

FIG. 7 is a block diagram of an exemplary embodiment of a sensorassembly 500 suitable for use as sensor assembly 300 (shown in FIGS. 3and 4). Sensor assembly 500 includes communications module 304 (shown inFIGS. 3 and 4), an impedance sensor 502, impedance sensor element 302(shown in FIG. 1), power module 308 (shown in FIGS. 3 and 4), a resetcontrol module 504, and a controller 505.

Controller 505 controls overall operation of sensor assembly 500.Controller 505 includes a processor 507 and a memory device 509. Inother embodiments, controller is an analog controller or an analog anddigital controller. Although illustrated as a discrete component,controller 505 is a description of control functions and aspects ofsensor assembly 500 and may actually be distributed about and performedby one or more other components of assembly 500.

Impedance sensor reader 502 includes a synthesizer 511 and a detector515. Synthesizer 511 includes a random access memory (RAM) memory device506, a local phase locked loop (PLL) 508, a digital-to-analog converter(DAC) 510, a tuned band pass filter array (BPF) 513, and a driver (DRV)514. DDS coefficients generated by remote computing device 106 (shown inFIG. 1) and received by assembly 500 through communications module 304are stored in memory device 506. In some embodiments, calibrationcoefficients for analog-to-digital conversion (ADC) are also stored inmemory device 506. In some embodiments, memory device 506 stores otherdata and/or instructions for impedance sensor 502 or any other componentof sensor assembly 500.

To span multiple decades of frequency, PLL 508 generates a multi-decadeclock reference for DAC 510. To reduce the power consumption in thereference generation, PLL 508 outputs a clock signal with relatively lowjitter, but without particularly low total harmonic distortion (THD).The reference clock is scaled by PLL 508 to allow several decades to bescanned with the same DDS coefficients. The clock is generated using alow power digital divider network 512 of fractional and/or integerdividers. In some embodiments, multiple crystal oscillators are used inPLL 508 instead of divider network 512. In still other embodiments aclock cleaner PLL is used to scale the reference clock. PLL 508 includesa voltage controlled oscillator (VCO), a mixer, charge pump and a Lowpass loop filter (LPF) to reject high frequency ripple in the mixer,charge pump response. The VCO is a ring oscillator with calibrationcoefficients specified along with the DDS coefficients. In otherembodiments, the VCO is a digitally controlled oscillator, for ease ofcalibration.

The DDS coefficients stored in memory device 506 and the clock signalgenerated by PLL 510 are used as inputs to DAC 510. DAC 510 uses the DDScoefficients and the clock signals to synthesize the excitation signalwith known signal characteristics. The output of DAC 510 is filtered bya tuned band pass filter array 513. A driver 514 supplies the filteredexcitation signal to impedance sensor element 302. Generating both theDAC calibration and the DDS coefficients at a host remotely from thesensor nodes, reduces the active power dissipation of the DAC and filtercircuitry. The response of impedance sensor element 302 to theexcitation signal is received detector 515. Detector 515 includes a lownoise amplifier (LNA) 516, which amplifies the signal and outputs theamplified response to a filter 518. The amplified and filtered responseis provided to an analog-to-digital converter (ADC) 520. AnAnti-Aliasing Filter (AAF) may be used prior to the ADC in someembodiments. Some embodiments include a mixer (not shown) betweenimpedance sensor element 302 and ADC 520 to reduce ADC sample rates. Thedigitized data is output from impedance sensor 502 for storage in memorydevice 509. In some embodiments, the data is stored in memory device506. In the exemplary embodiment, the raw output data is stored inmemory device 509 for transmission to remote computing device 106. Inother embodiments, averaging functions are applied by processor 507 tothe raw data to compress the amount of data that will need to betransmitted from sensor assembly 500 to remote computing device 106. Insome embodiments, processor 507 calculates an impedance of sensorelement 302 based on the raw data and stores the results of thecalculation for transmission to remote computing device 106.

Reset control module 504 includes a controller 522 and a power driver524. Controller 522 determines when to reset resonant antenna element302. Controller 522 uses power driver 524 to apply a relatively largeelectric current from power module 308 to resonant antenna element 302to reset resonant antenna element 302 as described above.

FIG. 8 is a block diagram of an exemplary embodiment of a power module600 suitable for use as power module 308 (shown in FIGS. 3 and 4). Powermodule 600 includes a PV panel 602, a power conditioner 604, a storagesystem 606, a power sensor 608, a control system 610, and an outputregulator 612.

PV panel 602 produces a direct current (DC) output in response to lightshining on the panel. The output is provided to power conditioner 604that conditions the power output to provide an output with the desiredcharacteristics, such as the desired voltage. Storage system 606 isconnected to the output of power conditioner 604. A charge controller614 charges energy storage device 616 to store energy for use when PVpanel 600 is not producing enough power for operation of sensor assembly500 (shown in FIG. 7), such as at night or when significant clouds arepresent. Power sensor 608 monitors the voltage (V) and current (I)output from conditioner 604. When the output is insufficient foroperation of sensor assembly 500, power sensor pulls additional powerfrom energy storage device 616.

Control system 610 is a power system controller that controls anddirects power module 600. Control system 610 monitors both the chargingand discharging rates of the storage device 616 and prevents storagesystem 606 from charging energy storage device 616 when the power outputby PV panel 602 is insufficient for the current operating needs ofsensor assembly 500. Control system 610 also monitors residual poweravailable and co-ordinates various power saving methods, such as scalingthe excitation power for DAC 510, the power of amplifier 516, and thescanning resolution of ADC 520 (all shown in FIG. 7) according to theamount of power available. Such methods save power at the cost of lessaccurate impedance measurements. A temperature sensor 618 monitors thetemperature of the power module and provides the result to controlsystem 610 so that control system 610 may take appropriate action ifexcessive temperatures occur.

The produced electric power of power module 600 is output from module600 using output regulators 612. In the exemplary embodiment regulators612 are low-drop out regulators (LDOs). In other embodiments, outputregulators 612 may be any suitable regulator.

FIG. 9 is a block diagram of another embodiment of an exemplary sensorassembly 700 that may be used as sensor assembly 104 in sensor network100 (both shown in FIG. 1). Sensor assembly 700 is similar to sensorassembly 300 (shown in FIGS. 3 and 4) and similar reference numerals areused to indicate similar components. Sensor assembly 700 compresses thedynamic range of the impedance scanning to further reduce powerrequirements of sensor assembly 700. Specifically, sensor assembly 700includes a reference impedance 702. Sensor assembly 700 rather thandetermining an absolute impedance of impedance sensor element 302 (shownin FIGS. 3 and 4), sensor assembly 700 differentially senses theimpedance variation between sensor element 302 and reference impedance702. This reduces the requirement of the DAC and ADC used in sensorassembly 700. Moreover the scan range is narrowed by coarse-finescanning to compress the number of points in the scanned spectrum.

FIG. 10 is a graph 800 of power requirements of various sensorassemblies that may be needed for use in sensor network 100 (shown inFIG. 1). The graph 800 charts the power requirements (in Watts) on alogarithmic scale for three different sensor assemblies as a function ofresolution of measurements of response of the impedance sensor elementthat is between 6 bits and 16 bits. Trace 802 represents the powerrequirements of a sensor assembly similar to assemblies 104 (shown inFIG. 1), 300 (shown in FIGS. 3 and 4), and 500 (shown in FIG. 7), butwhich determines its own DDS coefficients (local DDS determination).Above a resolution of about nine bits, this sensor assembly has thegreatest power requirements due because of the processing intensivecalculation of DDS coefficients. Trace 804 represents the powerrequirements of a sensor assembly similar to assemblies 104, 300, and500, which receives DDS coefficients from a remote computing device(referred to sometimes as a host computing device). Trace 806 representsthe power requirements of a sensor assembly similar to assembly 700(shown in FIG. 7), which receives DDS coefficients from a remotecomputing device and performs dynamic range (DR) compression. Below aresolution of about nine bits, all three traces 802, 804, 806 havesimilar power requirements because of relatively fixed minimum powerrequirements of the assemblies.

Sensor elements as described herein may be utilized, for example, formethane detection based a conventional Sn02 metal oxide composition. Anexample sensor element was built and operated in a resonant mode withmeasurements performed using built-in-house impedance sensor reader.This sensor operation provides multivariable response of the sensor. Thesensor was exposed to different concentrations of methane such as 0,111, 222, 444, 666, and 888 ppm in air to calibrate the sensor. Thesensor used a transfer function of:

CH₄ concentration (ppm)=A₀+A₁*SO₁+A₂*SO₂+A₃*SO₃+A₄*SO₄ where SO₁, SO₂,SO₃, and SO₄ are examples of outputs of a single multivariable sensorand A₀, A₁, A₂, A₃, and A₄ are examples of coefficients of the transferfunction.

Nonlimiting examples of outputs of a single multivariable sensor may bethe frequency of the maximum of the real part of the complex impedance(Fp, resonance peak position), magnitude of the real part of the compleximpedance (Zp, peak height), zero-reactance frequency (Fz, frequency atwhich the imaginary portion of impedance is zero), resonant frequency ofthe imaginary part of the complex impedance (F1), and anti-resonantfrequency of the imaginary part of the complex impedance (F2), signalmagnitude (Z1) at the resonant frequency of the imaginary part of thecomplex impedance (F1), and signal magnitude (Z2) at the anti-resonantfrequency of the imaginary part of the complex impedance (F2). Otherparameters may be measured using the entire complex impedance spectra,for example, quality factor of resonance, phase angle, and magnitude ofimpedance. Multivariable response spectral parameters are described inU.S. Pat. No. 7,911,345 entitled “Methods and systems for calibration ofRFID sensors”, U.S. Pat. App. Pub. No. 2014/0095102 entitled “Systemsand Methods of Monitoring Sensors”, and U.S. Pat. App. Pub. No.2014/0091811 entitled “Systems and Methods of Monitoring Sensors”.

FIG. 11 is a graph 1100 of the result of applying the developed transferfunction to display methane concentrations as measured with the methanesensor. These predicted methane concentrations are depicted in FIG. 11as a function of time, illustrating five replicate sets of measurementsof actual methane concentrations of 0, 111, 222, 444, 666, and 888 ppmin air. Each set of measurements of actual methane concentrations tookabout twelve and one half minutes, performing all five sets in aboutsixty-two and one half minutes. Thus, FIG. 11 shows the operation of thesensor calibrated for methane detection.

The calibrated sensor was further utilized to perform measurements ofmethane leaks outdoors. A model leak was generated using a methane gastank with 1% methane in air, having the tank to release its gascomposition through two-stage regulator and a valve, and detecting thegas leaks with the sensor at different distances from the gas tank. FIG.12 is a graph 1200 of the results of this outdoors stand-off detectionof methane leaks with the calibrated sensor during about 10 minutes timeof measurements. The first leak was detected at about one to two minutesas indicated by the methane concentration increase as detected by thesensor. At this time, the distance between the sensor and the source ofmethane leak was about five meters and the wind was directed from theleak to the sensor. Next, the leak was stopped by closing the valve andthe distance between the sensor and the source of methane leak wasdecreased to about three and one half meters. Upon opening the valve,the sensor detected the methane concentration increase at the time ofabout eight and one half to nine and one half minutes as indicated bythe methane concentration increase as detected by the sensor with thewind directed again from the leak to the sensor. Thus, FIG. 12 shows theoperation of the sensor calibrated for methane detection for thestand-off detection of methane leaks outdoors.

Self-powered sensor nodes, such as sensor assemblies 104, 300, 500, and700, offload DDS coefficient calculation to a remote computing device,resulting in lower power consumption as compared to some known sensornodes. Moreover, power requirements are further reduced throughimplementation of additional power saving techniques, such aspre-distorting DDS coefficients, using a local clock cleaner PLL, use ofa reference impedance, compression of data, and use of multiple lowerpower scans. The resulting lower power consumption permits the sensornodes to be self-powered by a reasonably sized, local resource, such asa small PV panel and a rechargeable energy storage device. Thereasonable size and self-powering of the sensor nodes allows them to beused in remote, hostile, and/or unpowered locations at which many knownsensors could not be used.

An exemplary technical effect of the methods, systems, and apparatusdescribed herein includes at least one of: (a) reducing thecomputational load on a sensor node through calculation of DDScoefficients at remote computing device; (b) reducing the powerrequirements of a sensor node; (c) permitting a sensor node to beself-powered by a local power source; (d) increasing the efficiency ofsensor nodes; and (e) extending the environments in which sensor nodesare useable.

Exemplary embodiments of the systems and methods are described above indetail. The systems and methods are not limited to the specificembodiments described herein, but rather, components of the systemsand/or steps of the methods may be utilized independently and separatelyfrom other components and/or steps described herein. For example, thesystem may also be used in combination with other apparatus, systems,and methods, and is not limited to practice with only the system asdescribed herein. Rather, the exemplary embodiment can be implementedand utilized in connection with many other applications. Althoughspecific features of various embodiments of the disclosure may be shownin some drawings and not in others, this is for convenience only. Inaccordance with the principles of the disclosure, any feature of adrawing may be referenced and/or claimed in combination with any featureof any other drawing.

Some embodiments involve the use of one or more electronic or computingdevices. Such devices typically include a processor, processing device,or controller, such as a general purpose central processing unit (CPU),a graphics processing unit (GPU), a microcontroller, a reducedinstruction set computer (RISC) processor, an application specificintegrated circuit (ASIC), a programmable logic circuit (PLC), a fieldprogrammable gate array (FPGA), a digital signal processing (DSP)device, and/or any other circuit or processing device capable ofexecuting the functions described herein. The methods described hereinmay be encoded as executable instructions embodied in a computerreadable medium, including, without limitation, a storage device and/ora memory device. Such instructions, when executed by a processingdevice, cause the processing device to perform at least a portion of themethods described herein. The above examples are exemplary only, andthus are not intended to limit in any way the definition and/or meaningof the term processor and processing device.

This written description uses examples to disclose the embodiments,including the best mode, and also to enable any person skilled in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

What is claimed is:
 1. A sensor network comprising: a plurality ofsensor assemblies, each sensor assembly of said plurality of sensorassemblies comprising: an impedance sensor element; a reset controlmodule including circuitry configured to reset the impedance sensorelement to an initial impedance and release adsorbed gases from saidimpedance sensor element, wherein resetting said impedance sensorelement is based on the impedance of said impedance sensor elementexceeding a threshold value; a communications module including circuitryconfigured to communicate with a computing device; and an impedancesensor reader coupled to said impedance sensor element and configured togenerate an excitation signal based on a plurality of direct digitalsynthesizer (DDS) coefficients and determine an impedance of saidimpedance sensor element based at least in part on a response of saidimpedance sensor element to the excitation signal; and a computingdevice communicatively coupled to at least one sensor assembly of saidplurality of sensor assemblies, said computing device comprising aprocessor and a memory device, said memory device storing instructionsto cause said computing device to: determine a plurality of DDScoefficients for at least one sensor assembly of said plurality ofsensor assemblies, the plurality of DDS coefficients determined toproduce the excitation signal in said at least one sensor assembly ofsaid plurality of sensor assemblies; and deliver the plurality of DDScoefficients to said at least one sensor assembly of said plurality ofsensor assemblies.
 2. The sensor network according to claim 1, whereineach sensor assembly of said plurality of sensor assemblies iscommunicatively coupled to said computing device through itscommunications module.
 3. The sensor network according to claim 1,wherein each sensor assembly of said plurality of sensor assemblies iscommunicatively coupled to at least one other sensor assembly throughits communications module, and at least one sensor assembly of saidplurality of sensor assemblies is communicatively coupled to saidcomputing device through its communications module.
 4. The sensornetwork according to claim 1, wherein said memory device storesinstructions that cause said computing device to pre-distort theplurality of DDS coefficients for at least one sensor assembly of saidplurality of sensor assemblies based on at least one operationalcharacteristic of said at least one sensor assembly.
 5. The sensornetwork according to claim 1, wherein the communications modules of theplurality of sensor assemblies comprise wireless communications modules.6. The sensor network according to claim 1, wherein the sensor networkis configured to perform monitoring of an industrial process.
 7. Thesensor network according to claim 6, wherein the industrial processincludes at least one of production of raw gas, material extraction,material transport, production of raw oil, operation of an internalcombustion engine, operation of an oil-filled transformer, a chemicalreaction process, a biological reaction process, purification and/orseparation process, a catalytic process, and a general combustionprocess.
 8. The sensor network according to claim 1, wherein theimpedance sensor element is configured to determine a measurand ofmethane, carbon monoxide, hydrocarbons, temperature, pH, or humidity. 9.The sensor network according to claim 1, wherein at least one sensorassembly of said plurality of sensor assemblies is self-powered andfurther comprises a power module including circuitry configured toprovide power to the at least one sensor assembly, the power moduleincluding a power source, an energy storage device, and a power modulecontroller.
 10. The sensor network according to claim 9, wherein thepower source includes a photovoltaic power source.
 11. The sensornetwork according to claim 9, wherein the power source includes anintegrated ultrasonic receiver.
 12. The sensor network according toclaim 9, wherein the power source includes an energy harvesting powersource.
 13. The sensor network according to claim 12, wherein the energyharvesting power source is configured to harvest one of solar, thermal,mechanical, vibrational, or ambient radio-frequency energy.
 14. Thesensor network according to claim 9, wherein the energy storage deviceincludes a battery, a capacitor, or a supercapacitor.
 15. The sensornetwork according to claim 1, wherein the plurality of sensor assembliesis powered by an array of photovoltaic modules including circuitryconfigured to provide power to the plurality of sensor assemblies. 16.The sensor network according to claim 15, wherein each sensor assemblyincluded in the plurality of sensor assemblies further comprises a powermodule controller configured to maximize the power produced by the arrayof photovoltaic modules by providing maximum power point tracking. 17.The sensor network according to claim 1, wherein the impedance sensorelement is a passive radio frequency identification sensor element. 18.The sensor network according to claim 17, wherein the passive radiofrequency identification sensor element includes an antenna circuitpartially covered by a sensing film.
 19. The sensor network according toclaim 1, wherein the impedance sensor element includes ainductor-capacitor-resistor sensor element, a thickness shear moderesonator sensor element, an acoustic wave sensor element, a surfaceacoustic wave sensor element, a tuning fork sensor element, or a splitring resonator sensor element.